Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Selected Papers from the AI-CyberSec 2021 Workshop in the 41st...
share announcement

Selected Papers from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4708

Special Issue Editors

Dr. Harsha Kalutarage

E-Mail Website
Guest Editor
School of Computing, Robert Gordon University, Aberdeen, UK
Interests: cybersecurity; AI for security; IoT security; network security
Prof. Dr. Nirmalie Wiratunga

E-Mail Website
Guest Editor
1. School of Computing, Robert Gordon University, Aberdeen, UK
2. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
Interests: Artificial Intelligence; case-based reasoning; data mining; text mining; machine learning
Dr. Sadiq Sani

E-Mail Website
Guest Editor
BT Applied Research, Adastral Park, Ipswich IP5 3RE, UK
Interests: Artificial Intelligence; natural language processing; machine learning; information retrieval; cybersecurity

Special Issue Information

Dear Colleagues,

The Artificial Intelligence and Cybersecurity Workshop (AI-CyberSec 2021 https://sites.google.com/view/ai-cybersec-2021/home) will be co-located with the 41st SGAI International Conference on Artificial Intelligence on December 14, 2021, in Cambridge, England. The workshop focuses on research that combines both AI and Cybersecurity and provides a unique forum for the exchange and discussion of new scientific contributions and open challenges in this research area. This Special Issue on "Selected papers from AI-CyberSec 2021 Workshop" expects to publish the invited extended versions of the contributions presented at AI-CyberSec 2021. The organising committee would like to invite submissions of novel theoretical and applied research in this area. The topics include (but are not limited to):

AI for cybersecurity:

  • AI and ML for intrusion detection/prevention and cyber attack mitigation
  • AI for cyber investigation and threat intelligence
  • AI and ML for biometric and continuous authentication
  • Integration of AI and Blockchain for security critical infrastructures
  • Applications of AI and ML for security orchestration, automation, and response
  • AI-based cybersecurity techniques for smart interconnected devices, IoT, smart cities
  • Advanced AI techniques to secure future Internet architectures/protocols
  • Explainability and interpretability of AI cybersecurity systems
  • Ethical aspects of AI cybersecurity systems

Malicious use of AI:

  • AI attacks with novel insights, techniques, or results
  • AI in attacks targeting cyber-physical systems, IoT, smart cities
  • Automation of tasks in cyber-offense
  • Automation of social engineering attacks
  • Automation of vulnerability discovery
  • Exploiting AI/ML used in cybersecurity systems

Cybersecurity for AI:

  • Cybersecurity of connected and autonomous devices
  • AI and risk management in AI supply chains and similar applications
  • Robustness of AI models
  • Privacy management, privacy-preserving ML and data leak prevention
  • Adversarial attacks on AI and defensive strategies

Note: As the workshop is virtual this year, there will be no fee for the workshop presenters or attendees. For the publication of the extended version in the Special Issue (open access, by invitation only), however, an APC can be charged by the journal. Selected authors can enjoy a discounted APC, which will be communicated with the invitation. APC for accepted papers shall be funded by the NTNU's IDUN project. Visit the workshop website for more details.

Dr. Harsha Kalutarage
Prof. Dr. Nirmalie Wiratunga
Dr. Sadiq Sani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1100 KiB  
Article
On the Need for Collaborative Intelligence in Cybersecurity
by Trevor Martin
Electronics 2022, 11(13), 2067; https://doi.org/10.3390/electronics11132067 - 30 Jun 2022
Cited by 2 | Viewed by 1500
Abstract
The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of [...] Read more.
The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity). We conclude with some suggestions for developing a hierarchical and graded representation. Full article
(This article belongs to the Special Issue Selected Papers from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence)
►▼ Show Figures

Figure 1

16 pages, 512 KiB  
Article
Bayesian Hyper-Parameter Optimisation for Malware Detection
by Fahad T. ALGorain and John A. Clark
Electronics 2022, 11(10), 1640; https://doi.org/10.3390/electronics11101640 - 20 May 2022
Cited by 3 | Viewed by 1905
Abstract
Malware detection is a major security concern and has been the subject of a great deal of research and development. Machine learning is a natural technology for addressing malware detection, and many researchers have investigated its use. However, the performance of machine learning [...] Read more.
Malware detection is a major security concern and has been the subject of a great deal of research and development. Machine learning is a natural technology for addressing malware detection, and many researchers have investigated its use. However, the performance of machine learning algorithms often depends significantly on parametric choices, so the question arises as to what parameter choices are optimal. In this paper, we investigate how best to tune the parameters of machine learning algorithms—a process generally known as hyper-parameter optimisation—in the context of malware detection. We examine the effects of some simple (model-free) ways of parameter tuning together with a state-of-the-art Bayesian model-building approach. Our work is carried out using Ember, a major published malware benchmark dataset of Windows Portable Execution metadata samples, and a smaller dataset from kaggle.com (also comprising Windows Portable Execution metadata). We demonstrate that optimal parameter choices may differ significantly from default choices and argue that hyper-parameter optimisation should be adopted as a ‘formal outer loop’ in the research and development of malware detection systems. We also argue that doing so is essential for the development of the discipline since it facilitates a fair comparison of competing machine learning algorithms applied to the malware detection problem. Full article
(This article belongs to the Special Issue Selected Papers from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop